Cookbook
Scikit-learn
Scikit-learn, commonly abbreviated as sklearn, is a popular open-source machine learning library. Scikit-learn offers a wide range of machine learning algorithms for tasks such as classification, regression, clustering, dimensionality reduction, and model selection. It also provides tools for data preprocessing, model evaluation, and cross-validation.
Decision Tree
Train Decision Tree in Python. Algorithm can be used in classification and regression ...Random Forest
Create Random Forest model for classification or regression task. In Advanced options ...k-Nearest Neighbors
Create k-Nearest Neighbors (k-NN) model for classification or regression.Train Model
Train Scikit-Learn model on provided data (X, y). It can be used with any model ...Compute Predictions
Compute prediction on provided data with Machine Learning model.Visualize Decision Tree
Visualize selected Decision Tree. Both classifier and regressor can be visualized.Hyper Parameters Search
Search for the best hyper parameters for your model. There are available two approached ...Feature Importance
Compute feature importance for any model (can be classifier or regressor) using ...Compute Metric
Compute metrics for predictions. This recipe supports following metrics: Accuracy, ...Confusion Matrix
Visualize the performance of your Machine Learning model with a confusion matrix.ROC Curve
Evaluate the performance of classifier with ROC (Receiver Operating Characteristic) ...Precision-Recall
Plot Precision-Recall curve.Lift Chart
Evaluate your classification model's performance with this Python code snippet that ...Calibration PLot
Evaluate your classification model's performance with this Python code snippet that ...
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